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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A method for encoding clinical datasets with SNOMED CT
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, September 2010
|
| DOI | 10.1186/1472-6947-10-53 |
| Pubmed ID | |
| Authors |
Dennis H Lee, Francis Y Lau, Hue Quan |
| Abstract |
Over the past decade there has been a growing body of literature on how the Systematised Nomenclature of Medicine Clinical Terms (SNOMED CT) can be implemented and used in different clinical settings. Yet, for those charged with incorporating SNOMED CT into their organisation's clinical applications and vocabulary systems, there are few detailed encoding instructions and examples available to show how this can be done and the issues involved. This paper describes a heuristic method that can be used to encode clinical terms in SNOMED CT and an illustration of how it was applied to encode an existing palliative care dataset. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 95 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Belgium | 2 | 2% |
| United Kingdom | 1 | 1% |
| Chile | 1 | 1% |
| Canada | 1 | 1% |
| Korea, Republic of | 1 | 1% |
| Unknown | 89 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 21 | 22% |
| Student > Ph. D. Student | 14 | 15% |
| Student > Master | 13 | 14% |
| Other | 11 | 12% |
| Student > Postgraduate | 9 | 9% |
| Other | 16 | 17% |
| Unknown | 11 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 35 | 37% |
| Medicine and Dentistry | 25 | 26% |
| Agricultural and Biological Sciences | 7 | 7% |
| Engineering | 5 | 5% |
| Nursing and Health Professions | 4 | 4% |
| Other | 7 | 7% |
| Unknown | 12 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 January 2014.
All research outputs
#6,217,321
of 22,738,543 outputs
Outputs from BMC Medical Informatics and Decision Making
#576
of 1,985 outputs
Outputs of similar age
#29,955
of 96,646 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#7
of 16 outputs
Altmetric has tracked 22,738,543 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 1,985 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 70% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 96,646 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.